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6 Trends Shaping Analytics Strategies in 2016

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The analytics initiatives that worked for companies in the past may prove insufficient as the year progresses.

If spending forecasts are any indication, businesses’ interest in analytics shows no sign of abating. International Data Corporation (IDC)¹ predicts that worldwide spending on external business analytics services will reach $101.9 billion by 2019, representing a compound annual growth rate of 14.7 percent. The research firm says adoption of new technologies and lack of internal skills is driving demand for these services.

Indeed, the talent shortage, the Internet of Things (IoT), and emerging cognitive technologies, among other trends, are influencing many companies’ strategies for analytics, according to research from Deloitte Consulting LLP. The research highlights six trends—some are new and some, like the talent shortage, are recurring—but all are significant for companies to consider as they formulate plans for extending their use of analytics.

Analytics expands across the enterprise. To date, many organizations have approached analytics opportunistically, setting up discrete capabilities to address specific business problems. While this incremental approach has proven effective, a growing number of companies now find they need to unify their disparate analytics efforts under a larger program. Some companies are already beginning to take these steps, with the goal of becoming “insight-driven organizations.”

Companies attempt to bridge the talent gap. IDC predicts² the U.S. will need 181,000 more people with deep analytical skills just two years from now. To meet this demand and find candidates with desired skills, some companies are working closely with universities’ analytics and data science programs to design internships and engage students in real-world projects. To position themselves as “employers of choice” for new graduates, companies should offer meaningful work, attractive career paths, and the opportunity to team with colleagues with similar skills and professional interests. Meanwhile, other companies are addressing the scarcity of analytics talent by engaging in ecosystems: They collaborate with multiple service providers to obtain different capabilities related to business intelligence, predictive analytics, data science, and cognitive technologies, for example.

Cybersecurity seeks help from analytics. Organizations with sophisticated cybersecurity programs use analytics to proactively identify and monitor potential cyberthreats. For example, automated tools scan Internet “chatter” looking for indications that a group or individual may be planning an attack. Predictive models based on past attack patterns can also help surface threats. And some analytics-based applications are designed to probe an organization’s defenses, aiming to identify vulnerabilities before hackers exploit them. Companies wishing to deploy analytics as part of their cyberthreat intelligence activities may find analytics and cyber professionals need to learn about each other’s domains to develop effective detection algorithms. And, as concerns about attacks mount, analytics teams may have to prioritize cybersecurity projects over others.

Businesses borrow from science. Long before businesses kindled an interest in analytics, universities, research labs, and other scientific organizations had been applying and refining mathematical models to solve complex problems in the fields of molecular biology, astrophysics, social science, and more. Now some businesses are applying analytical techniques—pioneered in scientific communities—to their challenges. While formal collaboration between the two is nascent, signs indicate a coming explosion in shared analytics tools, techniques, processes, and even talent. From major airlines and insurers to oil and gas explorers and beyond, businesses are actively hunting for science-based approaches to analytics that can give them an edge.

IoT fuels innovation. IoT is paving the way for a wide variety of data- and analytics-based innovations, a trend expected to peak within five years. Already, companies across industries are developing new services for customers based on connected data. For example, some auto insurance companies now use data from customers’ smartphones to power “pay-as-you-drive” products. Some health insurance providers offer discounts to customers who monitor their physical activity via wearable fitness devices. And in logistics industries, long-distance trucks and locomotives equipped with GPS and sensor technologies can take advantage of applications that optimize routes, analyze driving, and recommend service stations with competitive gas prices.

The man-machine dichotomy blurs. Advances in cognitive computing have shined a spotlight on the possibility of machines outperforming humans in a variety of functions, fueling concerns about technology-induced unemployment. While some job loss may result, smart machines will sooner complement, rather than replace, people. Nevertheless, interactions between humans and smart machines won’t happen seamlessly or automatically, and organizations will need to scrutinize knowledge-intensive processes to determine which tasks can best be performed by machines, and which still require the human touch. As they begin to explore applications for cognitive technologies, smart companies are simultaneously undertaking workforce planning exercises to determine the skills they’ll need over the next five to 10 years and considering ways to retrain staff whose roles may become obsolete.

6 Trends Shaping Analytics Strategies in 2016

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